from python_ai.common.xcommon import *
import numpy as np

X = [1, 2] # 输入t0,t1时刻的值[1,2]
state = [0.0, 0.0] # 状态
# 分开定义不同输入部分的权重以方便操作
w_cell_state = np.asarray([[0.1, 0.2], [0.3, 0.4]])  #单元隐藏层和state之间的参数
print(f'w_cell_state: {w_cell_state.shape}')
w_cell_input = np.asarray([0.5, 0.6])                #单元隐藏层和input输入之间的参数
print(f'w_cell_input: {w_cell_input.shape}')
b_cell = np.asarray([0.1, -0.1])                     #b是偏置项
print(f'b_cell: {b_cell.shape}')
# 定义用于输出的全连接层参数
w_output = np.asarray([[1.0], [2.0]])
print(f'w_output: {w_output.shape}')
b_output = 0.1
# 按照时间顺序执行循环神经网络的前向传播过程
for i in range(len(X)):
    sep(f't-{i+1}')
    # 计算循环体中的全连接层神经网络
    before_activation = np.dot(state, w_cell_state) + X[i] * w_cell_input + b_cell
    state = np.tanh(before_activation)
    #根据当前时刻状态计算最终输出
    final_output = np.dot(state, w_output) + b_output
    # 输出每个时刻的信息
    print("before activation:", before_activation)
    check_shape(before_activation, 'before_activation')
    print("state:", state)
    check_shape(state, 'state')
    print("output:", final_output)
    check_shape(final_output, 'final_output')
